
# Pandas / Numpy
import pandas as pd
import numpy as np
# NLTK - 3.8.1
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
# Regular Expressions
import re
import string
# Date/Time
import datetime
# sklearn - 1.2.2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay, precision_recall_curve
from sklearn.model_selection import cross_val_score, GridSearchCV
# Other Classification Models - LightGBM - 4.0.0 / XGBoost - 1.7.6 / CatBoost - 1.2
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
# Plotting
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
# News API
from newsapi import NewsApiClient
# Markdown
from IPython.display import Markdown, display
# Multi-Processing / Threading
from joblib import parallel_backend


# Download Stopwords
nltk.download("stopwords")
nltk.download("punkt")
nltk.download("wordnet")
# Set stop words
stopWords = stopwords.words("english")

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\SimonMurrell\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\SimonMurrell\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\SimonMurrell\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!


# Assign Colours
charcoalColour = "#63666A"
tealColour = "#00ACC9"
magentaColour = "#E6007E"
magentaColour = "#E6007E"
greenColour = "#80BA27"
lightGreenColour = "#B8DA25"
blueColour = "#50A2D4"
emeraldColour = "#05AF9A"
purpleColour = "#7E4CA5"
orangeColour = "#FF8300"
redColour = "#FA4616"
yellowColour = "#F3C317"


# News API Key - 1000 requests per 30 minutes
newsAPIKey = "d8061c628ce241bbb0a34d4b61fcd707"


# Assign Names
classificationName_nb = "Naive Bayes"
classificationName_lr = "Logistic Regression"
classificationName_svm = "Linear SVC"
classificationName_rf = "Random Forest"
classificationName_xg = "XG Boost"
classificationName_lg = "LightGBM"
classificationName_ab = "Adaptive Boosting"
classificationName_cb = "CatBoost"


# Load Real Datasets
real_dataset = pd.read_csv("./dataset/true.csv")
# Check Shape
real_dataset.shape

(21417, 4)


# Load Fake Dataset
fake_dataset = pd.read_csv("./dataset/fake.csv")
# Check Shape
fake_dataset.shape

(23481, 4)


# Check True Dataset
real_dataset.head()


# Check Fake Dataset
fake_dataset.head()


# Add Column
real_dataset["label"] = 1
fake_dataset["label"] = 0


# Drop Columns
real_dataset = real_dataset.drop("subject", axis=1)
real_dataset = real_dataset.drop("date", axis=1)
fake_dataset = fake_dataset.drop("subject", axis=1)
fake_dataset = fake_dataset.drop("date", axis=1)


# Check Shape
real_dataset.shape

(21417, 3)


# Check True Dataset
real_dataset.head()


# Check Shape
fake_dataset.shape

(23481, 3)


# Check Fake Dataset
fake_dataset.head()


def extractTextAfterHyphen(text):
    
    # Check Value
    parts = text.split("-")
    # Check Value
    if len(parts) > 1:
        
        # Return
        return parts[1].strip()
    
    else:
        
        # Return
        return text


# Strip out Prefix
real_dataset["text"] = real_dataset["text"].apply(extractTextAfterHyphen)


# Check Real Dataset
real_dataset.head()


# Combine Datasets
news_dataset = pd.concat([real_dataset, fake_dataset])


# Check Shape
news_dataset.shape

(44898, 3)


# Check Fake Dataset
news_dataset.head()


# Check Info
news_dataset.info()

<class 'pandas.core.frame.DataFrame'>
Index: 44898 entries, 0 to 23480
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   title   44898 non-null  object
 1   text    44898 non-null  object
 2   label   44898 non-null  int64 
dtypes: int64(1), object(2)
memory usage: 1.4+ MB


# Describe
news_dataset.describe()


# Get Label Value Counts
labelCounts = news_dataset["label"].value_counts()
# Get Label Mappings
labelMappings = {0: "Fake News", 1: "Real News"}
# Setup Colours
colours = [charcoalColour, tealColour]
# Create Pie Chart
plt.pie(labelCounts, labels=labelCounts.index.map(labelMappings), autopct="%1.1f%%", colors=colours)
# Set Legend
plt.legend(title="News Type", labels=["Fake News", "Real News"], loc="center left", bbox_to_anchor=(1, 0.5))
# Set Title
plt.title("Real vs Fake News")
# Show
plt.show()


# Check for missing values
missing_values = news_dataset.isnull().sum()
# Print 
missing_values

title    0
text     0
label    0
dtype: int64


# Check if there is any unrealiable data

# Print Missing Values
print(f'Missing Values')
print(f'Title: {missing_values.title}')
print(f'Text: {missing_values.text}')
print(f'Label: {missing_values.label}')
# Drop NA
news_dataset = news_dataset.dropna()
# Get Some Stats
news_dataset.describe()

Missing Values
Title: 0
Text: 0
Label: 0


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


# Lowercase words
news_dataset["title"] = news_dataset["title"].str.lower()
news_dataset["text"] = news_dataset["text"].str.lower()


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


def removeUrls(text):
    
    # Setup Pattern
    pattern = r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
    # Sub
    retVal = re.sub(pattern, "", text)
    # Return
    return retVal

# Remove URLs
news_dataset["title"] = news_dataset["title"].apply(removeUrls)
news_dataset["text"] = news_dataset["text"].apply(removeUrls)


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


# Remove Punctuation
removepunctuation = str.maketrans("", "", string.punctuation)

news_dataset["title"] = news_dataset["title"].str.translate(removepunctuation)
news_dataset["text"] = news_dataset["text"].str.translate(removepunctuation)


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


# Remove Numbers
news_dataset["title"] = news_dataset["title"].str.replace(r"\d+", "", regex=True)
news_dataset["text"] = news_dataset["text"].str.replace(r"\d+", "", regex=True)


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


def removeUnicode(text):
    
    # Return
    return text.encode("ascii", "ignore").decode("ascii")

# Remove Unicode
news_dataset["title"] = news_dataset["title"].apply(removeUnicode)
news_dataset["text"] = news_dataset["text"].apply(removeUnicode)


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


# Initialize WordNet Lemmatizer
lemmatizer = WordNetLemmatizer()


def lemmatizeText(text):
    
    # Extract Words from Text
    words = word_tokenize(text)
    # Lemmatize Words
    lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
    # Rejoin Words
    retVal = " ".join(lemmatized_words) 
    # Return
    return retVal

# Lemmatize Words
news_dataset["title"] = news_dataset["title"].apply(lemmatizeText)
news_dataset["text"] = news_dataset["text"].apply(lemmatizeText)


# Print Real News Head
news_dataset.head()


# Print Fake News Head
news_dataset[news_dataset["label"] == 0].head()


# Current techniques used in full project:

# Remove unreliable data
# Lowercase words
# Remove URLs
# Remove punctuation
# Remove Numbers
# Remove Unicode Characters
# Lemmatization / Stemming (Not Applied)
# Stop word removal - Not used as it reduced performance


# Setup Count Vectorizer
titleRealWordCounter = CountVectorizer()
# Fit / Transform Title
titleRealBow = titleRealWordCounter.fit_transform(news_dataset.loc[news_dataset["label"] == 1, "title"])
# Extract Feature Names
titleRealFeatureNames = titleRealWordCounter.get_feature_names_out()
# Get Word Counts
titleRealWordCount = titleRealBow.sum(axis=0)
# Setup Dictionary
titleRealWordCountDict = dict(zip(titleRealFeatureNames, titleRealWordCount.tolist()[0]))
# Sort Word Count Dictionary
titleRealSortedWordCountDict = sorted(titleRealWordCountDict.items(), key=lambda x: x[1], reverse=True)


# Generate DataFrame
dfTitleRealWords = pd.DataFrame(titleRealSortedWordCountDict)
# Assign Values
dfTitleRealWords = dfTitleRealWords.rename(columns={0: "word"})
dfTitleRealWords = dfTitleRealWords.rename(columns={1: "count"})
dfTitleRealWords["type"] = "title"
dfTitleRealWords["label"] = 1


# Setup Count Vectorizer
titleFakeWordCounter = CountVectorizer()
# Fit / Transform Title
titleFakeBow = titleFakeWordCounter.fit_transform(news_dataset.loc[news_dataset["label"] == 0, "title"])
# Extract Feature Names
titleFakeFeatureNames = titleFakeWordCounter.get_feature_names_out()
# Get Word Counts
titleFakeWordCount = titleFakeBow.sum(axis=0)
# Setup Dictionary
titleFakeWordCountDict = dict(zip(titleFakeFeatureNames, titleFakeWordCount.tolist()[0]))
# Sort Word Count Dictionary
titleFakeSortedWordCountDict = sorted(titleFakeWordCountDict.items(), key=lambda x: x[1], reverse=True)


# Generate DataFrame
dfTitleFakeWords = pd.DataFrame(titleFakeSortedWordCountDict)
# Assign Values
dfTitleFakeWords = dfTitleFakeWords.rename(columns={0: "word"})
dfTitleFakeWords = dfTitleFakeWords.rename(columns={1: "count"})
dfTitleFakeWords["type"] = "title"
dfTitleFakeWords["label"] = 0


# Setup Count Vectorizer
textRealWordCounter = CountVectorizer()
# Fit / Transform Text
textRealBow = textRealWordCounter.fit_transform(news_dataset.loc[news_dataset["label"] == 1, "text"])
# Extract Feature Names
textRealFeatureNames = textRealWordCounter.get_feature_names_out()
# Get Word Counts
textRealWordCount = textRealBow.sum(axis=0)
# Setup Dictionary
textRealWordCountDict = dict(zip(textRealFeatureNames, textRealWordCount.tolist()[0]))
# Sort Word Count Dictionary
textRealSortedWordCountDict = sorted(textRealWordCountDict.items(), key=lambda x: x[1], reverse=True)


# Generate DataFrame
dfTextRealWords = pd.DataFrame(textRealSortedWordCountDict)
# Assign Values
dfTextRealWords = dfTextRealWords.rename(columns={0: "word"})
dfTextRealWords = dfTextRealWords.rename(columns={1: "count"})
dfTextRealWords["word_length"] = dfTextRealWords["word"].apply(lambda x: len(x))
dfTextRealWords["type"] = "text"
dfTextRealWords["label"] = 1


# Setup Count Vectorizer
textFakeWordCounter = CountVectorizer()
# Fit / Transform Text
textFakeBow = textFakeWordCounter.fit_transform(news_dataset.loc[news_dataset["label"] == 0, "text"])
# Extract Feature Names
textFakeFeatureNames = textFakeWordCounter.get_feature_names_out()
# Get Word Counts
textFakeWordCount = textFakeBow.sum(axis=0)
# Setup Dictionary
textFakeWordCountDict = dict(zip(textFakeFeatureNames, textFakeWordCount.tolist()[0]))
# Sort Word Count Dictionary
textFakeSortedWordCountDict = sorted(textFakeWordCountDict.items(), key=lambda x: x[1], reverse=True)


# Generate DataFrame
dfTextFakeWords = pd.DataFrame(textFakeSortedWordCountDict)
# Assign Values
dfTextFakeWords = dfTextFakeWords.rename(columns={0: "word"})
dfTextFakeWords = dfTextFakeWords.rename(columns={1: "count"})
dfTextFakeWords["type"] = "text"
dfTextFakeWords["label"] = 0


# Get Title Words
dfTitleWords = pd.merge(dfTitleRealWords, dfTitleFakeWords, on="word", how="outer")
# Drop Columns
dfTitleWords = dfTitleWords.drop("type_x", axis=1)
dfTitleWords = dfTitleWords.drop("label_x", axis=1)
dfTitleWords = dfTitleWords.drop("type_y", axis=1)
dfTitleWords = dfTitleWords.drop("label_y", axis=1)
# Rename Columns
dfTitleWords = dfTitleWords.rename(columns={"count_x": "real_count"})
dfTitleWords = dfTitleWords.rename(columns={"count_y": "fake_count"})
# Remove stop words
dfTitleWords["word"] = dfTitleWords["word"].apply(lambda x: " ".join([word for word in x.split() if word.lower() not in stopWords]))
dfTitleWords = dfTitleWords[dfTitleWords["word"] != ""]
# Drop NA
dfTitleWords = dfTitleWords.dropna()
# Set Types
dfTitleWords["real_count"] = dfTitleWords["real_count"].astype(int)
dfTitleWords["fake_count"] = dfTitleWords["fake_count"].astype(int)
# Add Column
dfTitleWords["total_count"] = dfTitleWords["real_count"] + dfTitleWords["fake_count"]
# Sort
dfTitleWords = dfTitleWords.sort_values(by="total_count", ascending=False)

# Get Text Words
dfTextWords = pd.merge(dfTextRealWords, dfTextFakeWords, on="word", how="outer")
# Drop Columns
dfTextWords = dfTextWords.drop("type_x", axis=1)
dfTextWords = dfTextWords.drop("label_x", axis=1)
dfTextWords = dfTextWords.drop("type_y", axis=1)
dfTextWords = dfTextWords.drop("label_y", axis=1)
# Rename Columns
dfTextWords = dfTextWords.rename(columns={"count_x": "real_count"})
dfTextWords = dfTextWords.rename(columns={"count_y": "fake_count"})
# Remove stop words
dfTextWords["word"] = dfTextWords["word"].apply(lambda x: " ".join([word for word in x.split() if word.lower() not in stopWords]))
dfTextWords = dfTextWords[dfTextWords["word"] != ""]
# Drop NA
dfTextWords = dfTextWords.dropna()
# Set Types
dfTextWords["real_count"] = dfTextWords["real_count"].astype(int)
dfTextWords["fake_count"] = dfTextWords["fake_count"].astype(int)
# Add Column
dfTextWords["total_count"] = dfTextWords["real_count"] + dfTextWords["fake_count"]
# Sort
dfTextWords = dfTextWords.sort_values(by="total_count", ascending=False)


# Get Real Titles
dfRealTitles = news_dataset[news_dataset["label"] == 1]
# Drop Columns
dfRealTitles = dfRealTitles.drop("text", axis=1)
dfRealTitles = dfRealTitles.drop("label", axis=1)
# Add Column
dfRealTitles["title_length"] = dfRealTitles["title"].apply(lambda x: len(x))
# Remove Rubbish
dfRealTitles = dfRealTitles[dfRealTitles["title"] != ""]
# Sort
dfRealTitles = dfRealTitles.sort_values(by="title_length", ascending=True)

# Get Fake Titles
dfFakeTitles = news_dataset[news_dataset["label"] == 0]
# Drop Columns
dfFakeTitles = dfFakeTitles.drop("text", axis=1)
dfFakeTitles = dfFakeTitles.drop("label", axis=1)
# Add Column
dfFakeTitles["title_length"] = dfFakeTitles["title"].apply(lambda x: len(x))
# Remove Rubbish
dfFakeTitles = dfFakeTitles[dfFakeTitles["title"] != ""]
# Sort
dfFakeTitles = dfFakeTitles.sort_values(by="title_length", ascending=True)

# Get Real Title Length Average / Size
dfRealTitleLengths = dfRealTitles.groupby("title_length").size().reset_index(name="count")
# Get Real Title Length Average / Size
dfFakeTitleLengths = dfFakeTitles.groupby("title_length").size().reset_index(name="count")


# Set Lines
plt.plot(dfRealTitleLengths["title_length"], dfRealTitleLengths["count"], label="Real", c=charcoalColour)
plt.plot(dfFakeTitleLengths["title_length"], dfFakeTitleLengths["count"], label="Fake", c=tealColour)
# Set Title
plt.title("Article Title Lengths - Real vs Fake")
plt.xlabel("Title Lengths")
plt.ylabel("Count")
# Set Legend
plt.legend()
# Show
plt.show()


# Get Real Text
dfRealText = news_dataset[news_dataset["label"] == 1]
# Drop Columns
dfRealText = dfRealText.drop("title", axis=1)
dfRealText = dfRealText.drop("label", axis=1)
# Add Column
dfRealText["text_length"] = dfRealText["text"].apply(lambda x: len(x))
# Remove Rubbish
dfRealText = dfRealText[dfRealText["text_length"] > 250]
# Sort
dfRealText = dfRealText.sort_values(by="text_length", ascending=True)

# Get Fake Text
dfFakeText = news_dataset[news_dataset["label"] == 0]
# Drop Columns
dfFakeText = dfFakeText.drop("title", axis=1)
dfFakeText = dfFakeText.drop("label", axis=1)
# Add Column
dfFakeText["text_length"] = dfFakeText["text"].apply(lambda x: len(x))
# Remove Rubbish
dfFakeText = dfFakeText[dfFakeText["text_length"] > 250]
# Sort
dfFakeText = dfFakeText.sort_values(by="text_length", ascending=True)

# Get Real Text Length Average / Size
dfRealTextLengths = dfRealText.groupby("text_length").size().reset_index(name="count")
# Get Real Text Length Average / Size
dfFakeTextLengths = dfFakeText.groupby("text_length").size().reset_index(name="count")


# Set Scatter
plt.scatter(dfRealTextLengths["text_length"], dfRealTextLengths["count"], label="Real", c=charcoalColour)
plt.scatter(dfFakeTextLengths["text_length"], dfFakeTextLengths["count"], label="Fake", c=tealColour)
# Set Title
plt.title("Article Text Lengths - Real vs Fake")
plt.xlabel("Text Lengths")
plt.ylabel("Count")
# Set Legend
plt.legend()
# Show
plt.show()


# Get Real Titles
dfRealTitles = dfTitleRealWords
# Drop Columns
dfRealTitles = dfRealTitles.drop("count", axis=1)
dfRealTitles = dfRealTitles.drop("type", axis=1)
dfRealTitles = dfRealTitles.drop("label", axis=1)
# Add Column
dfRealTitles["word_length"] = dfRealTitles["word"].apply(lambda x: len(x))
# Sort
dfRealTitles = dfRealTitles.sort_values(by="word_length", ascending=True)

# Get Fake Titles
dfFakeTitles = dfTitleFakeWords
# Drop Columns
dfFakeTitles = dfFakeTitles.drop("count", axis=1)
dfFakeTitles = dfFakeTitles.drop("type", axis=1)
dfFakeTitles = dfFakeTitles.drop("label", axis=1)
# Add Column
dfFakeTitles["word_length"] = dfFakeTitles["word"].apply(lambda x: len(x))
# Sort
dfFakeTitles = dfFakeTitles.sort_values(by="word_length", ascending=True)

# Get Real Word Length Average / Size
dfRealWordLengths = dfRealTitles.groupby("word_length").size().reset_index(name="count")
# Get Real Word Length Average / Size
dfFakeWordLengths = dfFakeTitles.groupby("word_length").size().reset_index(name="count")


# Set Lines
plt.plot(dfRealWordLengths["word_length"], dfRealWordLengths["count"], label="Real", c=charcoalColour)
plt.plot(dfFakeWordLengths["word_length"], dfFakeWordLengths["count"], label="Fake", c=tealColour)
# Set Title
plt.title("Word Lengths - Real vs Fake")
plt.xlabel("Word Lengths")
plt.ylabel("Count")
# Set Legend
plt.legend()
# Show
plt.show()


# Assign Values
words = dfTitleWords["word"][:25]
real_counts = dfTitleWords["real_count"][:25]
fake_counts = dfTitleWords["fake_count"][:25]
# Set up horizontal bars
plt.barh(words, real_counts, label="Real Words", color=charcoalColour)
plt.barh(words, fake_counts, left=real_counts, label="Fake Words", color=tealColour)
# Set Titles
plt.xlabel("Counts")
plt.ylabel("Words")
plt.title("Top 25 Real vs Fake Words in Article Title")
# Set Legend
plt.legend()
# Show
plt.show()


# Assign Values
words = dfTextWords["word"][:25]
real_counts = dfTextWords["real_count"][:25]
fake_counts = dfTextWords["fake_count"][:25]
# Set up horizontal bars
plt.barh(words, real_counts, label="Real Words", color=charcoalColour)
plt.barh(words, fake_counts, left=real_counts, label="Fake Words", color=tealColour)
# Set Titles
plt.xlabel("Counts")
plt.ylabel("Words")
plt.title("Top 25 Real vs Fake Words in Article Text")
# Set Legend
plt.legend()
# Show
plt.show()


# Setup Label Encoder
labelEncoder = LabelEncoder()
# Train Dataset
X = news_dataset["title"] + " " + news_dataset["text"]
y = news_dataset["label"]
# Fit / Transform
y = labelEncoder.fit_transform(y)
# Train / Test Split based on 80/20 split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Setup Labels
labels = ["Fake", "Real"]


def buildPipeline(classifier, parameters_grid):
    
    # Print Status
    #print(f"Building Pipeline - " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    
    # Setup TfIdfVectorizer
    tfidfVectorizer = TfidfVectorizer(max_features=15000, ngram_range=(1, 2), min_df=2, max_df=0.6, sublinear_tf=True)
    # Setup Pipeline
    model = Pipeline(steps=[
        ("tfidfvectorizer", tfidfVectorizer),
        ("model", classifier)
    ])
    # Fit Model
    model.fit(X_train, y_train)
    
    # Check Value
    if (parameters_grid is not None):
    
        # Run across multi-processors
        with parallel_backend("multiprocessing"):

            # Setup Grid Search CV
            grid_search = GridSearchCV(model, parameters_grid, n_jobs=-1)
            # Fit Model
            grid_search.fit(X_train, y_train)

        # Re-assign Modal
        model = grid_search.best_estimator_

    # Predict
    y_pred = model.predict(X_test)
    
    # Print Status
    #print(f"Built Pipeline - " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    
    # Return
    return tfidfVectorizer, classifier, model, y_pred


def buildConfusionMatrix(y_test, y_pred):
    
    # Setup Confusion Matrix
    confusionMatrix = confusion_matrix(y_test, y_pred)
    # Switch
    confusionMatrix = [confusionMatrix[1], confusionMatrix[0]]
    # Setup Colours
    colours = [charcoalColour, tealColour]
    # Setup Heatmap
    figure_confusionMatrix = go.Figure(data=go.Heatmap(z=confusionMatrix,
                                      x=labels,
                                      y=labels[::-1],
                                      colorscale=colours,
                                      hoverinfo="z",
                                      showscale=False,
                                      text=confusionMatrix))
    # Build Classification Report
    classificationReport = classification_report(y_test, y_pred, output_dict=True)
    # Assign Accuracy
    accuracy = accuracy_score(y_test, y_pred)
    
    # Print Output Metrics
    print(classification_report(y_test, y_pred, target_names=labels))
    # Print Accuracy
    print("Accuracy:", accuracy)
    
    # Return
    return confusionMatrix, figure_confusionMatrix, classificationReport, accuracy


def appendMetrics(name, classificationReport, metrics):
    
    # Check Value
    if (name == "Naive Bayes"):
    
        # Setup Results
        metrics = pd.DataFrame({
            "Name": name,
            "Accuracy": str(round(classificationReport["accuracy"] * 100, 4)),
            "Precision": str(round(classificationReport["macro avg"]["precision"] * 100, 4)),
            "Recall": str(round(classificationReport["macro avg"]["recall"] * 100, 4)),
            "F1-Score": str(round(classificationReport["macro avg"]["f1-score"] * 100, 4))
            }, index=[0])
        
    else:
        
        # Setup Results
        newRow = pd.Series({
            "Name": name,
            "Accuracy": str(round(classificationReport["accuracy"] * 100, 4)),
            "Precision": str(round(classificationReport["macro avg"]["precision"] * 100, 4)),
            "Recall": str(round(classificationReport["macro avg"]["recall"] * 100, 4)),
            "F1-Score": str(round(classificationReport["macro avg"]["f1-score"] * 100, 4))
            })
        # Append Results
        metrics = pd.concat([metrics, newRow.to_frame().T], ignore_index=True)
        
    # Return
    return metrics


# Build Naive Bayes Pipeline
tfidfVectorizer_nb, classifier_nb, model_nb, y_pred_nb = buildPipeline(MultinomialNB(alpha=0.1, fit_prior=False), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_nb, fig_cm_nb, classification_nb, accuracy_nb = buildConfusionMatrix(y_test, y_pred_nb)
# Append Metrics
metrics = appendMetrics(classificationName_nb, classification_nb, None)

              precision    recall  f1-score   support

        Fake       0.96      0.97      0.96      4686
        Real       0.97      0.95      0.96      4294

    accuracy                           0.96      8980
   macro avg       0.96      0.96      0.96      8980
weighted avg       0.96      0.96      0.96      8980

Accuracy: 0.9628062360801781


# Build Logistic Regession Pipeline
tfidfVectorizer_lr, classifier_lr, model_lr, y_pred_lr = buildPipeline(LogisticRegression(C=50, n_jobs=-1, solver="liblinear"), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_lr, fig_cm_lr, classification_lr, accuracy_lr = buildConfusionMatrix(y_test, y_pred_lr)
# Append Metrics
metrics = appendMetrics(classificationName_lr, classification_lr, metrics)

C:\Users\SimonMurrell\anaconda3\envs\nlp\Lib\site-packages\sklearn\linear_model\_logistic.py:1222: UserWarning:

'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 20.

              precision    recall  f1-score   support

        Fake       0.99      1.00      1.00      4686
        Real       1.00      0.99      0.99      4294

    accuracy                           0.99      8980
   macro avg       0.99      0.99      0.99      8980
weighted avg       0.99      0.99      0.99      8980

Accuracy: 0.9948775055679288


# Build Linear SVM Pipeline
tfidfVectorizer_svm, classifier_svm, model_svm, y_pred_svm = buildPipeline(SVC(C=2, kernel="linear"), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_svm, fig_cm_svm, classification_svm, accuracy_svm = buildConfusionMatrix(y_test, y_pred_svm)
# Append Metrics
metrics = appendMetrics(classificationName_svm, classification_svm, metrics)

              precision    recall  f1-score   support

        Fake       1.00      0.99      0.99      4686
        Real       0.99      1.00      0.99      4294

    accuracy                           0.99      8980
   macro avg       0.99      0.99      0.99      8980
weighted avg       0.99      0.99      0.99      8980

Accuracy: 0.9944320712694877


# Build Random Forest Pipeline
tfidfVectorizer_rf, classifier_rf, model_rf, y_pred_rf = buildPipeline(RandomForestClassifier(n_estimators=150, max_depth=500, n_jobs=-1), None)
# Build Confusion Matrix Heat Map Figumre
confusionMatrix_rf, fig_cm_rf, classification_rf, accuracy_rf = buildConfusionMatrix(y_test, y_pred_rf)
# Append Metrics
metrics = appendMetrics(classificationName_rf, classification_rf, metrics)

              precision    recall  f1-score   support

        Fake       0.97      0.99      0.98      4686
        Real       0.99      0.97      0.98      4294

    accuracy                           0.98      8980
   macro avg       0.98      0.98      0.98      8980
weighted avg       0.98      0.98      0.98      8980

Accuracy: 0.9791759465478842


# Build XG Boost Pipeline
tfidfVectorizer_xg, classifier_xg, model_xg, y_pred_xg = buildPipeline(XGBClassifier(max_depth=7, n_estimators=250), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_xg, fig_cm_xg, classification_xg, accuracy_xg = buildConfusionMatrix(y_test, y_pred_xg)
# Append Metrics
metrics = appendMetrics(classificationName_xg, classification_xg, metrics)

              precision    recall  f1-score   support

        Fake       0.99      0.99      0.99      4686
        Real       0.99      0.99      0.99      4294

    accuracy                           0.99      8980
   macro avg       0.99      0.99      0.99      8980
weighted avg       0.99      0.99      0.99      8980

Accuracy: 0.9909799554565701


# Build LightGBM Pipeline
tfidfVectorizer_lg, classifier_lg, model_lg, y_pred_lg = buildPipeline(LGBMClassifier(boosting_type="gbdt", learning_rate=0.2, n_estimators=200, num_leaves=20), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_lg, fig_cm_lg, classification_lg, accuracy_lg = buildConfusionMatrix(y_test, y_pred_lg)
# Append Metrics
metrics = appendMetrics(classificationName_lg, classification_lg, metrics)

[LightGBM] [Info] Number of positive: 17123, number of negative: 18795
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 2.682172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1421730
[LightGBM] [Info] Number of data points in the train set: 35918, number of used features: 14996
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.476725 -> initscore=-0.093168
[LightGBM] [Info] Start training from score -0.093168
              precision    recall  f1-score   support

        Fake       0.99      0.99      0.99      4686
        Real       0.99      0.99      0.99      4294

    accuracy                           0.99      8980
   macro avg       0.99      0.99      0.99      8980
weighted avg       0.99      0.99      0.99      8980

Accuracy: 0.9926503340757238


# Build Adaptive Boosting Pipeline
tfidfVectorizer_ab, classifier_ab, model_ab, y_pred_ab = buildPipeline(AdaBoostClassifier(learning_rate=0.5, n_estimators=500), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_ab, fig_cm_ab, classification_ab, accuracy_ab = buildConfusionMatrix(y_test, y_pred_ab)
# Append Metrics
metrics = appendMetrics(classificationName_ab, classification_ab, metrics)

              precision    recall  f1-score   support

        Fake       0.99      0.99      0.99      4686
        Real       0.99      0.99      0.99      4294

    accuracy                           0.99      8980
   macro avg       0.99      0.99      0.99      8980
weighted avg       0.99      0.99      0.99      8980

Accuracy: 0.9913140311804008


# Build Cat Boosting Pipeline
tfidfVectorizer_cb, classifier_cb, model_cb, y_pred_cb = buildPipeline(CatBoostClassifier(learning_rate=0.1, iterations=1000, depth=6, verbose=False), None)
# Build Confusion Matrix Heat Map Figure
confusionMatrix_cb, fig_cm_cb, classification_cb, accuracy_cb = buildConfusionMatrix(y_test, y_pred_cb)
# Append Metrics
metrics = appendMetrics(classificationName_cb, classification_cb, metrics)

              precision    recall  f1-score   support

        Fake       0.99      0.99      0.99      4686
        Real       0.99      0.99      0.99      4294

    accuracy                           0.99      8980
   macro avg       0.99      0.99      0.99      8980
weighted avg       0.99      0.99      0.99      8980

Accuracy: 0.9902004454342984


def addConfusionMatrixFigure(fig, figure, row, col):
    
    # Add Trace
    fig.add_trace(figure, row, col)
    # Add Annotations
    annotations = figure["text"]
    # Search through Rows
    for i in range(len(annotations)):
        
        # Search through Rows
        for j in range(len(annotations[i])):
            
            # Add Annotation
            fig.add_annotation(
                x=j,
                y=i,
                text=str(annotations[i][j]),
                showarrow=False,
                font=dict(color="white", size=12),
                xref="x",
                yref="y",
                row=row, 
                col=col
            )


# Set up Sub Plots
fig = make_subplots(rows=4, 
                    cols=2, 
                    subplot_titles=(classificationName_nb, 
                                    classificationName_lr, 
                                    classificationName_svm, 
                                    classificationName_rf, 
                                    classificationName_xg, 
                                    classificationName_lg, 
                                    classificationName_ab, 
                                    classificationName_cb))
# Add 
addConfusionMatrixFigure(fig, fig_cm_nb.data[0], 1, 1)
addConfusionMatrixFigure(fig, fig_cm_lr.data[0], row=1, col=2)
addConfusionMatrixFigure(fig, fig_cm_svm.data[0], row=2, col=1)
addConfusionMatrixFigure(fig, fig_cm_rf.data[0], row=2, col=2)
addConfusionMatrixFigure(fig, fig_cm_xg.data[0], row=3, col=1)
addConfusionMatrixFigure(fig, fig_cm_lg.data[0], row=3, col=2)
addConfusionMatrixFigure(fig, fig_cm_ab.data[0], row=4, col=1)
addConfusionMatrixFigure(fig, fig_cm_cb.data[0], row=4, col=2)
# Update Title
fig.update_layout(title="Classification Models Confusion Matrices", 
                  title_x=0.5,
                  height=1000)
# Show Chart
fig.show()


# Update Data Frame
metrics["Accuracy"] = metrics["Accuracy"].astype(float)
metrics["Precision"] = metrics["Precision"].astype(float)
metrics["Recall"] = metrics["Recall"].astype(float)
metrics["F1-Score"] = metrics["F1-Score"].astype(float)


# Sort Results
metrics = metrics.copy().sort_values(by=["Accuracy"], ascending=False)
# Assign Value
metrics["Accuracy"] = metrics["Accuracy"]
# Copy DataFrame
metrics_cvs = metrics.copy()
# Output Results
metrics


# Assign Chart Colours
chart_colours = [charcoalColour, tealColour, lightGreenColour, purpleColour]
chart_colours_dict = { "Accuracy": charcoalColour, "Precision": tealColour, "Recall": lightGreenColour, "F1-Score": purpleColour}


def addMetricFigure(fig, metric, row, col):
    
    # Plot Bar Chart
    fig_metric = go.Figure()
    # Assign Metrics
    fig_metric.add_trace(go.Scatter(x=metrics["Name"], y=metrics[metric], mode="lines+markers", name=metric, marker=dict(color=chart_colours_dict[metric], size=8)))
    # Assign Text
    fig_metric.add_trace(go.Scatter(x=metrics["Name"], y=metrics[metric], mode="text", name=metric, text=metrics[metric].round(2), textposition="top right"))
    # Add Sub Plots
    fig.add_trace(fig_metric.data[0], row=row, col=col)


# Set up Sub Plots
fig = make_subplots(rows=2, 
                    cols=2, 
                    subplot_titles=("Accuracy", "Precision", "Recall", "F1-Score"))
# Add 
addMetricFigure(fig, "Accuracy", 1, 1)
addMetricFigure(fig, "Precision", 1, 2)
addMetricFigure(fig, "Recall", 2, 1)
addMetricFigure(fig, "F1-Score", 2, 2)
# Update Title
fig.update_layout(title="Classification Models Subplots", 
                  title_x=0.5,
                  height=800)
# Show Chart
fig.show()


# Plot Bar Chart
fig = px.bar(metrics, 
             x="Name", 
             y=["Accuracy", "Precision", "Recall", "F1-Score"], 
             barmode="group",
             color_discrete_map=chart_colours_dict)
# Apply Titles and Axis Titles
fig.update_layout(title="Interactive Performance Metrics",
                  title_x=0.5,
                  height=700,
                  xaxis_title="Classification Model",
                  yaxis_title="Percentage")
# Limit Range
fig.update_yaxes(range=[95, 100])
# Show Chart
fig.show()


# Assign Metric Scoring
scores = {
    "Accuracy": "accuracy",
    "Precision": "precision",
    "Recall": "recall",
    "F1-Score": "f1"
}


def crossValidateMetric(model, name, X, y, cv=5):
        
    # Print Status
    #print(f"Started {name} - " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    
    # Search through Items
    for metric, scoring in scores.items():
        
        # Run Cross Validation Score
        results = cross_val_score(model, X, y, cv=cv, scoring=scoring, n_jobs=3)
        # Assign Value
        row = metrics_cvs[metrics_cvs["Name"] == name]
        # Assign Values
        metrics_cvs.loc[row.index, metric + " CVS"] = np.mean(results) * 100
        
    # Print Status
    #print(f"Completed {name} - " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))


# Assign Value
cv = 3
# Cross Validation Classification Models
crossValidateMetric(model_nb, classificationName_nb, X, y, cv)
crossValidateMetric(model_lr, classificationName_lr, X, y, cv)
crossValidateMetric(model_svm, classificationName_svm, X, y, cv)
crossValidateMetric(model_rf, classificationName_rf, X, y, cv)
crossValidateMetric(model_xg, classificationName_xg, X, y, cv)
crossValidateMetric(model_lg, classificationName_lg, X, y, cv)
crossValidateMetric(model_ab, classificationName_ab, X, y, cv)
crossValidateMetric(model_cb, classificationName_cb, X, y, cv)


# Print Out Metrics
metrics_cvs


def analyseNewsArticle(title, text):

    # Assign Sentence
    sentence = title + " - " + text

    # Apply Preprocessing

    # Lowercase
    sentence = sentence.lower()
    # Remove URLs
    sentence = removeUrls(sentence)
    # Remove Punctuation
    sentence = sentence.translate(removepunctuation)
    # Remove Numbers
    sentence = re.sub(r"\d+", "", sentence)
    # Remove Unicode
    sentence = removeUnicode(sentence)
    # Lemmatize Words
    sentence = lemmatizeText(sentence)
    
    # Run through Pipelines

    # Transform Sentence
    sentence_vectorized = tfidfVectorizer_nb.transform([sentence])
    
    # Make Predictions 
    prediction = classifier_nb.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_lr.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_svm.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_rf.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_xg.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_lg.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_ab.predict(sentence_vectorized)[0]
    prediction = prediction + classifier_cb.predict(sentence_vectorized)[0]

    # Check Value
    if prediction >= 5:
        return "Real"
    else:
        return "Fake"


# Initialize NewsAPI
newsapi = NewsApiClient(api_key=newsAPIKey)


# Get Top Headlines - BBC News / CNN / NBC News / Russian TV
top_headlines = newsapi.get_top_headlines(q="",
                                          sources="bbc-news,cnn,nbc-news,rt",
                                          language="en", 
                                          page_size=20)


# Check Value
if top_headlines["status"] == "ok":
    
    # Print Spacer
    display(Markdown(f"#\n# Fake / Real News Analaysis:\n#"))
    
    # Go through each article
    for article in top_headlines["articles"]:
        
        # Assign Values
        title = article["title"]
        text = article["content"]
        url = article["url"]
        publishedDate = article["publishedAt"]
        publishedDate = publishedDate.replace("T", " ")
        publishedDate = publishedDate[:10]
        titleLink = f'<a href="{url}" target="_blank">{title}</a>'
        
        # Check Text
        if text is not None:
        
            # Analyse Article
            status = analyseNewsArticle(title, text)
            # Check article if Real / Fake
            display(Markdown(f"**Title:** {titleLink}\n**Published Date:** {publishedDate}\n**Status:** {status}"))
            
    # Print Spacer
    display(Markdown("#"))

	title	text	subject	date
0	As U.S. budget fight looms, Republicans flip t...	WASHINGTON (Reuters) - The head of a conservat...	politicsNews	December 31, 2017
1	U.S. military to accept transgender recruits o...	WASHINGTON (Reuters) - Transgender people will...	politicsNews	December 29, 2017
2	Senior U.S. Republican senator: 'Let Mr. Muell...	WASHINGTON (Reuters) - The special counsel inv...	politicsNews	December 31, 2017
3	FBI Russia probe helped by Australian diplomat...	WASHINGTON (Reuters) - Trump campaign adviser ...	politicsNews	December 30, 2017
4	Trump wants Postal Service to charge 'much mor...	SEATTLE/WASHINGTON (Reuters) - President Donal...	politicsNews	December 29, 2017

	title	text	subject	date
0	Donald Trump Sends Out Embarrassing New Year’...	Donald Trump just couldn t wish all Americans ...	News	December 31, 2017
1	Drunk Bragging Trump Staffer Started Russian ...	House Intelligence Committee Chairman Devin Nu...	News	December 31, 2017
2	Sheriff David Clarke Becomes An Internet Joke...	On Friday, it was revealed that former Milwauk...	News	December 30, 2017
3	Trump Is So Obsessed He Even Has Obama’s Name...	On Christmas day, Donald Trump announced that ...	News	December 29, 2017
4	Pope Francis Just Called Out Donald Trump Dur...	Pope Francis used his annual Christmas Day mes...	News	December 25, 2017

	title	text	label
0	As U.S. budget fight looms, Republicans flip t...	WASHINGTON (Reuters) - The head of a conservat...	1
1	U.S. military to accept transgender recruits o...	WASHINGTON (Reuters) - Transgender people will...	1
2	Senior U.S. Republican senator: 'Let Mr. Muell...	WASHINGTON (Reuters) - The special counsel inv...	1
3	FBI Russia probe helped by Australian diplomat...	WASHINGTON (Reuters) - Trump campaign adviser ...	1
4	Trump wants Postal Service to charge 'much mor...	SEATTLE/WASHINGTON (Reuters) - President Donal...	1

	title	text
0	Donald Trump Sends Out Embarrassing New Year’...	Donald Trump just couldn t wish all Americans ...
1	Drunk Bragging Trump Staffer Started Russian ...	House Intelligence Committee Chairman Devin Nu...
2	Sheriff David Clarke Becomes An Internet Joke...	On Friday, it was revealed that former Milwauk...
3	Trump Is So Obsessed He Even Has Obama’s Name...	On Christmas day, Donald Trump announced that ...
4	Pope Francis Just Called Out Donald Trump Dur...	Pope Francis used his annual Christmas Day mes...

	title	text	label
0	As U.S. budget fight looms, Republicans flip t...	The head of a conservative Republican faction ...	1
1	U.S. military to accept transgender recruits o...	Transgender people will be allowed for the fir...	1
2	Senior U.S. Republican senator: 'Let Mr. Muell...	The special counsel investigation of links bet...	1
3	FBI Russia probe helped by Australian diplomat...	Trump campaign adviser George Papadopoulos tol...	1
4	Trump wants Postal Service to charge 'much mor...	President Donald Trump called on the U.S. Post...	1

	label
count	44898.000000
mean	0.477015
std	0.499477
min	0.000000
25%	0.000000
50%	0.000000
75%	1.000000
max	1.000000

	title	text	label
0	as us budget fight looms republicans flip thei...	the head of a conservative republican faction ...	1
1	us military to accept transgender recruits on ...	transgender people will be allowed for the fir...	1
2	senior us republican senator let mr mueller do...	the special counsel investigation of links bet...	1
3	fbi russia probe helped by australian diplomat...	trump campaign adviser george papadopoulos tol...	1
4	trump wants postal service to charge much more...	president donald trump called on the us postal...	1

	title	text	label
0	a u budget fight loom republican flip their fi...	the head of a conservative republican faction ...	1
1	u military to accept transgender recruit on mo...	transgender people will be allowed for the fir...	1
2	senior u republican senator let mr mueller do ...	the special counsel investigation of link betw...	1
3	fbi russia probe helped by australian diplomat...	trump campaign adviser george papadopoulos tol...	1
4	trump want postal service to charge much more ...	president donald trump called on the u postal ...	1

	Name	Accuracy	Precision	Recall	F1-Score
1	Logistic Regression	99.4878	99.4887	99.4848	99.4868
2	Linear SVC	99.4432	99.4376	99.4470	99.4422
5	LightGBM	99.2650	99.2618	99.2656	99.2637
6	Adaptive Boosting	99.1314	99.1199	99.1414	99.1299
4	XG Boost	99.0980	99.0958	99.0968	99.0963
7	CatBoost	99.0200	99.0155	99.0211	99.0182
3	Random Forest	97.9176	97.9850	97.8635	97.9112
0	Naive Bayes	96.2806	96.3296	96.2297	96.2699

	title	text
0	donald trump sends out embarrassing new year’...	donald trump just couldn t wish all americans ...
1	drunk bragging trump staffer started russian ...	house intelligence committee chairman devin nu...
2	sheriff david clarke becomes an internet joke...	on friday, it was revealed that former milwauk...
3	trump is so obsessed he even has obama’s name...	on christmas day, donald trump announced that ...
4	pope francis just called out donald trump dur...	pope francis used his annual christmas day mes...

	title	text
0	donald trump sends out embarrassing new year’...	donald trump just couldn t wish all americans ...
1	drunk bragging trump staffer started russian ...	house intelligence committee chairman devin nu...
2	sheriff david clarke becomes an internet joke...	on friday, it was revealed that former milwauk...
3	trump is so obsessed he even has obama’s name...	on christmas day, donald trump announced that ...
4	pope francis just called out donald trump dur...	pope francis used his annual christmas day mes...

	title	text
0	donald trump sends out embarrassing new year’...	donald trump just couldn t wish all americans ...
1	drunk bragging trump staffer started russian ...	house intelligence committee chairman devin nu...
2	sheriff david clarke becomes an internet joke...	on friday it was revealed that former milwauke...
3	trump is so obsessed he even has obama’s name...	on christmas day donald trump announced that h...
4	pope francis just called out donald trump dur...	pope francis used his annual christmas day mes...

	title	text
0	donald trump sends out embarrassing new year’...	donald trump just couldn t wish all americans ...
1	drunk bragging trump staffer started russian ...	house intelligence committee chairman devin nu...
2	sheriff david clarke becomes an internet joke...	on friday it was revealed that former milwauke...
3	trump is so obsessed he even has obama’s name...	on christmas day donald trump announced that h...
4	pope francis just called out donald trump dur...	pope francis used his annual christmas day mes...

Library Imports¶

Load up Libraries / Stopwords¶

Load / Clean up Dataset¶

Load up Datasets / Do Initial Inspection¶

Combine Datasets¶

Strip out Prefix in Real News¶

Info / Describe Dataset¶

Real vs Fake News Breakdown¶

Pre-processing Data¶

Do Initial Inspection¶

Remove Invalid Data¶

Lowercase Characters¶

Remove URLs¶

Remove Punctuation¶

Remove Numbers¶

Remove Unicode¶

Lemmatize Words¶

Title / Text / Word Analysis¶

Build Datasets for Analysis¶

Article Title Lengths - Real vs Fake¶

Article Text Lengths - Real vs Fake¶

Word Lengths - Real vs Fake¶

Top 25 Real vs Fake Words in Article Title¶

Top 25 Real vs Fake Words in Article Text¶

Split up Train / Testing Data¶

Build Classification Pipelines¶

Naive Bayes Classification Model¶

Logistic Regression Classification Model¶

Linear SVM Classification Model¶

Random Forest Classification Model¶

XG Boost Classification Model¶

LightGBM Classification Model¶

Adaptive Boosting Classification Model¶

Cat Boost Classification Model¶

Confusion Matrices¶

Summarised Performance Metrics¶

Visualisations / Graphing¶

Overall Performance Metrics (Line Chart per Metric)¶

Overall Performance Metrics (Bar Chart Combined)¶

Cross Validation Scoring¶

Real-time News Analysis¶

News API Analysis¶

Fake / Real News Analaysis:¶